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Cloud Memory Manage Tool

memory_manage

Store and retrieve team-scoped semantic notes across conversations. Supports search, add, delete, and bulk upload to remember preferences, constraints, and decisions.

Instructions

Team-scoped semantic memory — short notes agents store and retrieve across conversations (preferences, constraints, prior decisions). Backed by pgvector with HNSW indexing; cross-team isolation enforced by TeamScope. Distinct from knowledge_manage: this stores unstructured short notes (1-2 paragraphs), knowledge_manage stores document corpora.

When to use: an agent needs to recall a stable fact about the user/team across runs, or wants to remember a decision so the next conversation starts with that context. Do NOT use for transient run-state — use the experiment artifact system instead.

Actions:

  • search (read; semantic) — query (string), optional limit (default 20, max 100). Returns ranked entries with similarity score 0..1; entries below threshold 0.6 are filtered.

  • list_recent (read) — optional limit (default 50). Reverse-chronological by created_at.

  • stats (read) — returns { total_entries, embedding_dim, last_write_at, storage_bytes }.

  • add (write — costs ~1 credit per ~1k tokens for embedding) — content (string, max 4000 chars), optional metadata (object).

  • delete (DESTRUCTIVE) — memory_id (UUID). Hard delete; not recoverable.

  • upload_knowledge (write — costs embedding credits, async) — one of: file content (string), url. Bulk-ingests into memory; returns job_id. Up to 50 MB per call.

Cloud note: supabase_provision not available in cloud (provisioning happens at platform level). Errors: 401, 403, 404 if memory_id missing, 413 on oversized payload, 422 validation, 429 rate limit, 503 if budget exhausted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform: search, list_recent, stats, add, delete, upload_knowledge
deadline_msNoOptional: max wall-clock time (ms) the tool may spend. If exceeded during the call, returns a DEADLINE_EXCEEDED error. Minimum 100 ms. Leave unset for no deadline.
queryYesSearch keyword to match against memory content
agent_idNoFilter by agent UUID
limitNoMax results to return (default 10, max 100)
min_confidenceNoMinimum confidence score to filter results (0.0–1.0, default 0.0 to include all)
categoryNoFilter by memory category: preference, knowledge, context, behavior, goal
search_modeNoRetrieval mode: semantic=flat keyword search, local=1-hop graph traversal from matched entities, global=high-centrality entities, hybrid=semantic+local merged, mix=semantic+global mergedsemantic
tagsNoFilter by tags — only return memories containing ANY of these tags. E.g. ["barsy:client", "barsy:shared"]. Omit to return all memories regardless of tags.
topicNoNamespace pre-filter by topic slug, e.g. "auth_migration". Narrows the search to a named context before the vector scan for higher precision.
source_typeNoFilter by source type (e.g. execution, manual, signal)
contentYesThe memory text to store
project_idNoAssociate this memory with a specific project UUID (optional)
confidenceNoConfidence score 0.0–1.0. Default: 1.0 for manually added memories
metadataNoAdditional structured metadata (key-value pairs)
memory_idsYesArray of memory UUIDs to delete
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description details read/write actions, destructive delete, embedding costs, async upload, errors list (401,403,404,413,422,429,503), and cloud provision note. However, lacks explicit mention of authorization requirements and pagination behavior for list_recent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured: purpose first, then usage guidance, then action list. Each section serves a purpose, though the action list could be slightly more compact. No wasted sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers actions, errors, and cloud note. Lacks explicit return structure for most actions (only search and stats are described). No output schema, so description should explain all return values; missing for list_recent, add, delete, upload_knowledge.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context for actions (e.g., search default limit 20, max 100) but does not significantly enhance parameter meanings beyond what the schema provides. No unique insights per parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as 'Team-scoped semantic memory' for short notes, and explicitly distinguishes it from sibling knowledge_manage by noting scope and content type (unstructured short notes vs document corpora).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States when to use: to recall stable facts across runs or remember decisions. Explicitly provides a negative case: 'Do NOT use for transient run-state — use the experiment artifact system instead.' Clearly identifies alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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